Open Set Domain Adaptation by Backpropagation(OSBP)论文复现(非官方方法) 1.准备数据集 MNIST数据集:28*28,共70000张图片,10类数字 USPS数据集:16*16,共20000张图片,10类数字 SVHN数据集:32*32,共73257张图片,10类数字 由于torchvision.datasets中自带的数据集没有USPS数据集,所以使用一个类设置数据集 """...
《 Open Set Domain Adaptation by Backpropagation 》Kuniaki Saito,Shohei Yamamoto,Yoshitaka Ushiku,Tatsuya Harada Open Set 开放集(open set)与之前的数据集(通常是close set)主要区别在于 target 中是否包含 source 中不存在的类别。Open set 在 domain adaptation 领域中是由 Ref [1] 提出,在实际场景中,模型...
模型结构需具体描述，但整体思路围绕Open Set Domain Adaptation by Backpropagation(OSBP)展开。训练过程中，使用SVHN数据集中编号为0到4的样本进行训练，而对MNIST数据集则使用全部样本进行训练。训练结果的代码可参考：github.com/redhat12345/...
Open Set Domain Adaptation by Backpropagation(OSBP)论文数字数据集复现 1.准备数据集 2.模型结构 3.训练(SVHN→→ MNIST) 4.结果1|11.准备数据集MNIST数据集:28*28,共70000张图片,10类数字USPS数据集:16*16,共20000张图片,10类数字SVHN数据集:32*32,共73257张图片,10类数字...
domainadaptationopensetbackpropagationsamples OpenSetDomainAdaptationbyBackpropagationKuniakiSaito1,ShoheiYamamoto1,YoshitakaUshiku1,andTatsuyaHarada1,21TheUniversityofTokyo,2RIKEN{k-saito,yamamoto,ushiku,harada}@mi.t.u-tokyo.ac.jpAbstract.Numerousalgorithmshavebeenproposedfortransferringknowledgefromalabel-richdoma...
Domain adaptationOpen set recognition Adversarial learningNumerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for a closed-set scenario, where the source and the target domain ...
[5] Saito, Kuniaki, et al. "Open set domain adaptation by backpropagation." Proceedings of the European Conference on Computer Vision (ECCV). 2018. [6] Zhang, Hongjie, et al. "Improving Open Set Domain Adaptation Using Image-to-Image Translation." 2019 IEEE International Conference on Multim...
本文对Open set domain adaptation by back propagation(OSDA-BP)中用于提取潜在未知类别样本的二元交叉熵损失进行了深入的研究。基于这种新的理解,我们提出用对称的库勒贝克-莱布勒距离损失来代替二元交叉熵损失。1|31.INTRODUCTION AND RELATED WORK作者透彻详尽地解释了对于OSDA-BP中二元交叉熵损失的理解,并使用对称的...
openset-DA This is an unofficial pytorch implementation ofOpen Set Domain Adaptation by Backpropagation. Requirements Python 3.5+ PyTorch 0.4 torchvision scikit-learn Usage Run SVHN -> MNIST python train.py --task s2m --gpu <gpu_id> Run USPS -> MNIST ...
improved open set domain adaptation with backpropagation 学习笔记 文章目录 improved open set domain adaptation with backpropagation 学习笔记 TIP ABSTRACT 1.INTRODUCTION AND RELATED WORK 2.PROPOSED METHOD 2.1 Over... 用As3 Air 准备写个类似于Tile的工具了 ...